Estimates Of Species Abundance Research Articles

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Diversity of Parasitoid Wasps and Comparison of Sampling Strategies in Rice Fields Using Metabarcoding.

A comprehensive and precise evaluation of Arthropoda diversity in agricultural landscapes can enhance biological pest control strategies. We used Malaise traps and sweep nets to collect insects from three double-cropping paddy fields. DNA was extracted from the ethanol preservative of the Malaise traps and from tissue samples of selected parasitoid wasps. This was followed by amplification using DNA barcoding primers to prepare high-throughput sequencing libraries. We annotated a total of 4956 operational taxonomic units (OTUs), encompassing 174 genera and 32 families of parasitoid wasps. The ethanol filter method efficiently captured a wide range of information. However, the method has low resolution and may result in a reduced estimate of species abundance. Additional insect species were also identified in the parasitoid samples. This suggests that high throughput sequencing from adult parasitoid wasps can also detect host species, enabling a better understanding of host species and providing insights into food webs.

Sea turtle density surface models along the United States Atlantic coast

Spatially explicit estimates of marine species distribution and abundance are required to quantify potential impacts from human activities such as military training and testing, fisheries interactions, and offshore energy development. There are 4 protected species of sea turtle (loggerhead, green, Kemp’s ridley, and leatherback) commonly found along the east coast of the USA, our study area, and which require impact assessments. Data from 7 different survey organizations were used to create density surface models for the 4 sea turtle species utilizing 1.2 million km of line-transect surveys. A substantial portion (29.7%) of available sightings were not identified to the species level. Not including these sightings would underestimate density, so a conditional random forest model was used to assign unidentified sightings to species. Higher densities of loggerhead, green, and Kemp’s ridley sea turtles were predicted south of the Outer Banks in cool months, transitioning northwards in late spring to occupy seasonal neritic habitats. The highest leatherback densities were predicted off the coasts of Georgia and Florida. Leatherbacks were also predicted throughout offshore areas. The predicted distribution patterns generally matched satellite tracking and strandings data, indicating the models reproduced established seasonal movements. Surveys rarely detect sea turtles smaller than 40 cm, so these age classes are not represented. The models are the first for the study area to apply availability bias estimates developed in or near the study area and attempt to classify unidentified sightings to the species level, providing an updated, critical tool for conservation management along the eastern seaboard.

Taking the bait: Environmental factors affecting bait retention and hook disposition in the Gulf of Maine Bottom Longline Survey

Longline fishery surveys are major contributors to groundfish species abundance estimates. Catchability is often assumed constant, but bait is lost or consumed at inconsistent rates during and across gear sets. Accounting for hook availability and gear saturation can improve abundance estimates, but these data are challenging to collect. Our objectives were to determine whether video camera systems can be used to collect these data from the Northeast Fisheries Science Center’s Gulf of Maine Bottom Longline Survey and to explore the associations between hook disposition percentages and environmental characteristics. Electronic monitoring camera systems were deployed on partner vessels during two fall and two spring seasons to capture video of gear retrieval. During post-survey review, individual hooks were recorded as one of the following hook dispositions: bait, empty hook, fish, and invertebrate. Percentages of these hook dispositions were characterized across categorical (season, day or night, and bottom substrata) and continuous (bottom temperature, bottom depth, and substrate rugosity) survey station variables. Nonmetric multidimensional scaling and logistic regression analyses revealed correlations between retained bait and low bottom temperatures, the spring season, shallow depths, rough bottom substrata, and high terrain rugosity. These characteristics were typical of shallow, inshore, and topographically variable survey strata, suggesting that catchability differs among habitat types. This information will be incorporated into catch rate models to develop standardized indices of abundance for groundfish stock assessments. This study is an example of the utility of electronic technologies to supplement on board data collection during cooperative fishery surveys.

Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools

BackgroundAlthough the spread of antimicrobial resistance (AMR) through food and its production poses a significant concern, there is limited research on the prevalence of AMR bacteria in various agri-food products. Sequencing technologies are increasingly being used to track the spread of AMR genes (ARGs) in bacteria, and metagenomics has the potential to bypass some of the limitations of single isolate characterization by allowing simultaneous analysis of the agri-food product microbiome and associated resistome. However, metagenomics may still be hindered by methodological biases, presence of eukaryotic DNA, and difficulties in detecting low abundance targets within an attainable sequence coverage. The goal of this study was to assess whether limits of detection of ARGs in agri-food metagenomes were influenced by sample type and bioinformatic approaches.ResultsWe simulated metagenomes containing different proportions of AMR pathogens and analysed them for taxonomic composition and ARGs using several common bioinformatic tools. Kraken2/Bracken estimates of species abundance were closest to expected values. However, analysis by both Kraken2/Bracken indicated presence of organisms not included in the synthetic metagenomes. Metaphlan3/Metaphlan4 analysis of community composition was more specific but with lower sensitivity than the Kraken2/Bracken analysis. Accurate detection of ARGs dropped drastically below 5X isolate genome coverage. However, it was sometimes possible to detect ARGs and closely related alleles at lower coverage levels if using a lower ARG-target coverage cutoff (< 80%). While KMA and CARD-RGI only predicted presence of expected ARG-targets or closely related gene-alleles, SRST2 (which allows read to map to multiple targets) falsely reported presence of distantly related ARGs at all isolate genome coverage levels. The presence of background microbiota in metagenomes influenced the accuracy of ARG detection by KMA, resulting in mcr-1 detection at 0.1X isolate coverage in the lettuce but not in the beef metagenome.ConclusionsThis study demonstrates accurate detection of ARGs in synthetic metagenomes using various bioinformatic methods, provided that reads from the ARG-encoding organism exceed approximately 5X isolate coverage (i.e. 0.4% of a 40 million read metagenome). While lowering thresholds for target gene detection improved sensitivity, this led to the identification of alternative ARG-alleles, potentially confounding the identification of critical ARGs in the resistome. Further advancements in sequencing technologies providing increased coverage depth or extended read lengths may improve ARG detection in agri-food metagenomic samples, enabling use of this approach for tracking clinically important ARGs in agri-food samples.

OccupancyTuts: Occupancy modelling tutorials with RPresence

Abstract The occupancy modelling framework offers tremendous flexibility in estimating species abundance and distribution patterns while accounting for imperfect detection, and has seen rapid growth and adoption since its introduction at the beginning of the century. At the same time, in an era of big data, there are increasing demands on developing quantitative skills and proficiency in young ecologists, many of whom lack the quantitative training needed to conduct research professionally. We introduce occupancyTuts, an R package that features 28 learnr tutorials that teach the statistical underpinnings of several occupancy models. The tutorials include written content, instructional videos, R exercises, and quiz elements, covering a range of topics including statistical underpinnings, single‐ and dynamic‐occupancy models, study design and several of the ‘spin‐off’ models that extend the basic framework. We plan for development of new tutorials that use RPresence as the analysis engine, and welcome new tutorial contributions that use other R packages as the analysis engine as well.

Enumeration potential of environmental DNA for Pacific salmon stock assessments

AbstractThe field of environmental DNA (eDNA) has advanced over the past decade, with multiple approaches available for a variety of sampling media and species. While using eDNA for the purpose of simply detecting species is becoming a routine process, the utility of eDNA to estimate species abundance is not well understood. Here, we quantify salmon environmental DNA upstream of a fish counting fence along with river velocity, and together, use these values to determine the correlation between the number of salmon passing by the fish fence daily with daily eDNA rates in water before, during, and after the salmon spawning season for four Pacific salmonids (Oncorhynchus gorbuscha, O. kisutch, O. tshawytscha, and O. nerka; pink, coho, chinook, and sockeye, respectively). Throughout the spawning season, approximately 182,000 salmon were counted passing through the fence, of which &gt;98% were pink salmon. Pink salmon exhibited strong correlation between human counts (effect size = 0.65, SE = 0.040) to eDNA rates in the present study and exhibited day‐to‐day variation and a unimodal profile rising and falling with human fish counts. However, the salmon species observed in much lower numbers exhibited a much weaker correlation with eDNA levels higher during the pre‐migratory period than during the migratory period for sockeye, coho, and chinook. Thus, for salmon spawning runs with less than ~1000 adults and daily counts of less than ~100, the juvenile and/or prior seasons eDNA signal appears to be indistinguishable from the adult spawning eDNA signal in our river system. However, for the large pink salmon run, eDNA rates appeared to reflect a local signal of salmon in space and time, essentially tracking these fish within days of passing through the eDNA sampling site.

Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation

Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation

Abundance estimation with DNA metabarcoding – recent advancements for terrestrial arthropods

Biodiversity is declining at alarming rates worldwide and large-scale monitoring is urgently needed to understand changes and their drivers. While classical taxonomic identification of species is time and labour intensive, the combination with DNA-based methods could upscale monitoring activities to achieve larger spatial coverage and increased sampling effort. However, challenges remain for DNA-based methods when the number of individuals per species and/or biomass estimates are required. Several methodological advancements exist to improve the potential of DNA metabarcoding for abundance analysis, which however need further evaluation. Here, we discuss laboratory, as well as some bioinformatic adjustments to DNA metabarcoding workflows regarding their potential to achieve species abundance estimation from arthropod community samples. Our review includes pre-laboratory processing methods such as specimen photography, laboratory methods such as the use of spike-in DNA as an internal standard and bioinformatic advancements like correction factors. We conclude that specimen photography coupled with DNA metabarcoding currently promises the greatest potential to achieve estimates of the number of individuals per species and biomass estimates, but that approaches such as spike-ins and correction factors are promising methods to pursue further.

Removal of false positives in metagenomics-based taxonomy profiling via targeting Type IIB restriction sites

Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Mock samples resolve biases in diversity estimates and quantitative interpretation of zooplankton metabarcoding data

Metabarcoding is a rapidly developing tool in marine zooplankton ecology, although most zooplankton surveys continue to rely on visual identification for monitoring purposes. We attempted to resolve some of the biases associated with metabarcoding by sequencing a 313-b.p. fragment of the COI gene in 34 “mock” samples from the North Sea which were pre-sorted to species level, with biomass and abundance estimates obtained for each species and taxonomic group. The samples were preserved either in 97% ethanol or dehydrated for 24 h in a drying oven at 65 °C (the routine way of preserving samples for dry weight measurements). The visual identification yielded a total of 59 unique holoplanktonic and 16 meroplanktonic species/taxa. Metabarcoding identified 86 holoplanktonic and 124 meroplanktonic species/taxa, which included all but 3 of the species identified visually as well as numerous species of hard-to-identify crustaceans, hydrozoan jellyfish, and larvae of benthic animals. On a sample-to-sample basis, typically 90–95% of visually registered species were recovered, but the number of false positives was also high. We demonstrate robust correlations of relative sequence abundances to relative biomass for most taxonomic groups and develop conversion factors for different taxa to account for sequencing biases. We then combine the adjusted sequencing data with a single bulk biomass measurement for the entire sample to produce a quantitative parameter akin to species biomass. When examined with multivariate statistics, this parameter, which we call BWSR (biomass-weighed sequence reads) showed very similar trends to species biomass and comparable patterns to species abundance, highlighting the potential of metabarcoding not only for biodiversity estimation and mapping of presence/absence of species but also for quantitative assessment of zooplankton communities.

Building Software for Hierarchical Events in Biodiversity Informatics

In 2019, the Atlas of Living Australia (ALA) ran a national consultation, clarifying a long-held suspicion that while simple occurrence records provide invaluable discoverability and analysis for biodiversity data, the lack of contextual information on data collection methodology and protocols limits its usefulness for species abundance estimation and time-series analysis. The consultation recognised that the ALA has strong leadership in biodiversity standards and development, and that our 12-year history and investment in projects and engagement demonstrates a clear capacity to transition to a repository capable of capturing and aggregating the monitoring and survey data required for conservation efforts (Daly 2019). Around the same time, the larger data landscape was undergoing change in a similar direction, both internationally through the Global Biodiversity Information Facility’s (GBIF) Unified Model engagements, and nationally through the development of the Australian Biodiversity Information Standard (ABIS), an ontology for describing environmental data (Anonymous 2021). We embarked on a project to examine existing data standards and practices, extend our own occurrence model, and build software that could ingest event-based datasets and make them discoverable and interoperable. Initially we focused on well-structured surveys, both marine and terrestrial, to develop the system and user interface (UI). During the project, we restructured and modeled other exemplar datasets, collaborating with GBIF to develop event terms, vocabularies, and user interface components. Seeking interoperability with existing standards, we integrated concepts from both ABIS and the Ocean Biodiversity Information System’s (OBIS) ENV-DATA model (De Pooter et al. 2017) into a standardised yet flexible implementation of Event Core, navigable via a friendly user interface. The initial software release is comprised of an ingestion pipeline for events in parallel to occurrences, an index capable of handling nested data structures, and a user interface. The UI guides the user to explore and filter datasets; includes visualisations for data structures, taxonomic scope, repeat location surveys, extended measurements or facts; and links out to child occurrence records. Users can download filtered original and interpreted datasets with Digital Object Identifiers (DOI), in compressed files that comply simultaneously with Darwin Core Archive and Frictionless Data Package specifications. On release, we will present a range of datasets covering different event-based scenarios. The model has serendipitously provided the flexibility to encapsulate complex seed bank data. During the project, we developed a draft extension, which we used to service a new data portal for the Australian Seed Bank Partnership, a testament to the model’s serviceability for novel use cases. The ALA has taken innovative steps beyond simple collection of complex data types and worked with our local biodiversity informatics community to provide a navigable interface to this data. We intend to continue working with our own data providers and the international community, to realise the benefits of a more complex data model.

A semi‐automated camera trap distance sampling approach for population density estimation

AbstractCamera traps have become important tools for the monitoring of animal populations. However, the study‐specific estimation of animal detection probabilities is key if unbiased abundance estimates of unmarked species are to be obtained. Since this process can be very time‐consuming, we developed the first semi‐automated workflow for animals of any size and shape to estimate detection probabilities and population densities. In order to obtain observation distances, a deep learning algorithm is used to create relative depth images that are calibrated with a small set of reference photos for each location, with distances then extracted for animals automatically detected by MegaDetector 4.0. Animal detection by MegaDetector was generally independent of the distance to the camera trap for 10 animal species at two different study sites. If an animal was detected both manually and automatically, the difference in the distance estimates was often minimal at a distance about 4 m from the camera trap. The difference increased approximately linearly for larger distances. Nonetheless, population density estimates based on manual and semi‐automated camera trap distance sampling workflows did not differ significantly. Our results show that a readily available software for semi‐automated distance estimation can reliably be used within a camera trap distance sampling workflow, reducing the time required for data processing, by &gt;13‐fold. This greatly improves the accessibility of camera trap distance sampling for wildlife research and management.

Endophytic fungal communities isolated from two genotypes of feijoa fruits (Feijoa sellowiana O. Berg.) and prospection of potential agents against anthracnose pathogens

Feijoa sellowiana (O. Berg) Burret is a tropical fruit from South America known as pineapple-guava or feijoa. Anthracnose is a fungal-caused disease that harms feijoa, and may completely reduce fruit production. The endophytic microbiota of feijoa fruits from native cultivars may provide new biocontrol agents against anthracnose. This work aimed to evaluate the diversity of endophytic cultivable fungi in feijoa fruits from two Brazilian native cultivars and verify potential agents against anthracnose pathogens: Colletotrichum nymphaeae and Colletotrichum siamense. The cultivable fungal diversity in feijoa fruits was assessed by fungi isolation, followed by fungal identification, through sequencing the ITS region of rDNA, and estimation of species abundance and frequency. Later, the antagonism of selected isolates was tested against Colletotrichum spp. by direct confrontation. Results demonstrated that isolated fungal communities slightly differed between the two cultivars. There was no difference in the Shannon index between the two cultivars. However, abundance and frequency of endophytes was respectively, 40% and 53% higher in the most resistant cultivar (Mattos), where Trichoderma harzianum, Trichoderma hamatum, Clonostachys rosea and Clonostachys rhizophaga were exclusively found. C. rosea and T. harzianum from cultivar 'Mattos', and Talaromices amestolkiae found in both cultivars ('Mattos' and 'Alcantara'), reduced mycelial growth and sporulation of Colletotrichum spp. Therefore, the endophytic cultivable community was slightly more abundant and rich in the most resistant cultivar of feijoa by encompassing more antagonistic species of fungi. This work also described for the first time the potential of endophytic fungi as biocontrol agents of feijoa pathogens.

Adapting the sampling design of research surveys to improve the biomass estimation of secondary species—the case study of Raja clavata

Abstract Research surveys are important to evaluate fishery resources’ spatial distribution and abundance. Although the underlying sampling is usually conceived with a focus on specific species, efficient designs can also collect data on secondary species. We present a framework to explore and evaluate the adequacy of alternative sampling designs for fishery research surveys aiming to maximize accuracy estimates of the secondary species abundance while maintaining the quality of the abundance estimates of primary species. A geostatistical model-based approach was developed considering the semi-continuous nature of the data and the excess of zero values commonly observed for secondary species. New sampling designs were defined according to optimization weights and evaluated based on the resulting prediction exactness. The framework was applied to the bottom trawl survey conducted along the Portuguese continental coast with European hake, Merluccius merluccius, as the primary species and thornback ray, Raja clavata, as the secondary species. The sampling design URSI provided the best balance between the accuracy for both primary and secondary species. The methodology can be replicated for other bottom trawl research surveys and an extended set of species. We recommend that a decision on which sampling design to adopt in future surveys should consider a cost-efficiency analysis.

Toward absolute abundance for conservation applications: Estimating the number of contributors via microhaplotype genotyping of mixed-DNA samples.

Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon (Oncorhynchus tshawytscha) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

Improved species level bacterial characterization from rhizosphere soil of wilt infected Punica granatum

Pomegranate crops are prone to wilt complex disease, which is known to severely hamper the crop yield. There have been limited studies that have explored bacteria–plant–host associations in wilt complex disease affecting pomegranate crops. In the present study, wilt infected rhizosphere soil samples (ISI, ASI) in pomegranate were studied in comparison to a healthy control (HSC). The 16S metagenomics sequencing approach using the MinION platform was employed for screening of bacterial communities and predictive functional pathways. Altered physicochemical properties in the soil samples were recorded showing a comparatively acidic pH in the ISI (6.35) and ASI (6.63) soil samples to the HSC soil (7.66), along with higher electrical conductivity in the ISI (139.5 µS/cm), ASI soil (180 µS/cm), HSC soil sample (123.33 µS/cm). While concentration of micronutrients such as Cl and B were significantly higher in the ISI and ASI soil as compared to the HSC, Cu and Zn were significantly higher in the ASI soil. The effectiveness and accuracy of 16S metagenomics studies in identifying beneficial and pathogenic bacterial communities in multi-pathogen–host systems depend on the completeness and consistency of the available 16S rRNA sequence repositories. Enhancing these repositories could significantly improve the exploratory potential of such studies. Thus, multiple 16S rRNA data repositories (RDP, GTDB, EzBioCloud, SILVA, and GreenGenes) were benchmarked, and the findings indicated that SILVA yields the most reliable matches. Consequently, SILVA was chosen for further analysis at the species level. Relative abundance estimates of bacterial species showed variations of growth promoting bacteria, namely, Staphylococcus epidermidis, Bacillus subtilis, Bacillus megatarium, Pseudomonas aeruginosa, Pseudomonas putida, Pseudomonas stutzeri and Micrococcus luteus. Functional profiling predictions employing PICRUSt2 revealed a number of enriched pathways such as transporter protein families involved in signalling and cellular processes, iron complex transport system substrate binding protein, peptidoglycan biosynthesis II (staphylococci) and TCA cycle VII (acetate-producers). In line with past reports, results suggest that an acidic pH along with the bioavailability of micronutrients such as Fe and Mn could be facilitating the prevalence and virulence of Fusarium oxysporum, a known causative pathogen, against the host and beneficial bacterial communities. This study identifies bacterial communities taking into account the physicochemical and other abiotic soil parameters in wilt-affected pomegranate crops. The insights obtained could be instrumental in developing effective management strategies to enhance crop yield and mitigate the impact of wilt complex disease on pomegranate crops.

Open‐source workflow approaches to passive acoustic monitoring of bats

AbstractThe affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non‐invasive, cost‐effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user‐friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats.We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open‐source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst.Workflow performance depended strongly on the signal‐to‐noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large‐scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance.We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open‐source software such as PAMGuard and exemplify how detection choices, whether pre‐ or post‐deployment, hardware or software‐driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.

State-of-the-art of data analyses in environmental DNA approaches towards its applicability to sustainable fisheries management

An increasing number of studies using marine environmental DNA (eDNA) approaches are showing its potential application in marine fisheries management by helping and simplifying some of the labor-intensive traditional surveys required to assess exploited populations and ecosystem status. eDNA approaches (i.e. metabarcoding and targeted) can support to ecosystem-based fisheries management by providing information on species composition; surveillance of invasive, rare and/or endangered species; and providing estimates of species abundance. Due to these potential uses in fisheries and conservation sciences, the number of studies applying eDNA approaches in marine habitats has expanded in the very last few years. However, a lack of consistency across studies when applying pipelines for data analyses, makes results difficult to compare among them. Such lack of consistency is partially caused by poor knowledge in the management of raw sequences data, and analytical methods allowing comparative results. Hence, we review here the essential steps of eDNA data processing and analyses to get sound, reproducible, and comparable results, providing a set of bioinformatics tools useful for each step. Altogether this review presents the state of the art of eDNA data analyses towards a comprehensive application in fisheries management promoting sustainability.

How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design

AbstractExtra‐organismal DNA (eoDNA) from material left behind by organisms (noninvasive DNA, e.g., feces, hair) or from environmental samples (eDNA, e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost‐ and time‐intensive replications of genotyping/sequencing procedures. Therefore, system‐ and site‐specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using 10 environmentally diverse bat roosts as a case study, we developed a robust modeling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling‐site‐specific ideal exposure duration. Maximum humidity was the strongest eoDNA‐degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site‐specific simulations revealed that reducing exposure duration to 2–8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA‐based species detection, and increasing the accuracy of abundance estimates.